{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#| hide\n",
    "!pip install -Uqq nixtla utilsforecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#| hide \n",
    "from nixtla.utils import in_colab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#| hide \n",
    "IN_COLAB = in_colab()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#| hide\n",
    "if not IN_COLAB:\n",
    "    from nixtla.utils import colab_badge\n",
    "    from dotenv import load_dotenv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forecasting web traffic\n",
    "\n",
    "Our task is to forecast the next 7 days of daily visits to the website [cienciadedatos.net](cienciadedatos.net).\n",
    "\n",
    "In this tutorial we will show:\n",
    "\n",
    "* How to load time series data to be used for forecasting with TimeGPT\n",
    "\n",
    "* How to create cross-validated forecasts with TimeGPT\n",
    "\n",
    "This tutorial is an adaptation from [Joaquín Amat Rodrigo, Javier Escobar Ortiz, \"Forecasting web traffic with machine learning and Python\"](https://cienciadedatos.net/documentos/py37-forecasting-web-traffic-machine-learning.html). We will show you:\n",
    "\n",
    "* how you can achieve almost 10% better forecasting results;\n",
    "\n",
    "* using significantly less lines of code;\n",
    "\n",
    "* in a fraction of the time needed to run the original tutorial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "[![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Nixtla/nixtla/blob/main/nbs/docs/use-cases/1_forecasting_web_traffic.ipynb)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#| echo: false\n",
    "if not IN_COLAB:\n",
    "    load_dotenv()    \n",
    "    colab_badge('docs/use-cases/1_forecasting_web_traffic')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Import packages\n",
    "First, we import the required packages and initialize the Nixtla client."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from nixtla import NixtlaClient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nixtla_client = NixtlaClient(\n",
    "    # defaults to os.environ.get(\"NIXTLA_API_KEY\")\n",
    "    api_key = 'my_api_key_provided_by_nixtla'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 👍 Use an Azure AI endpoint\n",
    "> \n",
    "> To use an Azure AI endpoint, remember to set also the `base_url` argument:\n",
    "> \n",
    "> `nixtla_client = NixtlaClient(base_url=\"you azure ai endpoint\", api_key=\"your api_key\")`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#| hide\n",
    "if not IN_COLAB:\n",
    "    nixtla_client = NixtlaClient()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Load data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We load the website visit data, and set it to the right format to use with TimeGPT. In this case, we only need to add an identifier column for the timeseries, which we will call `daily_visits`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>users</th>\n",
       "      <th>unique_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>2324</td>\n",
       "      <td>daily_visits</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2201</td>\n",
       "      <td>daily_visits</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-07-03</td>\n",
       "      <td>2146</td>\n",
       "      <td>daily_visits</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-07-04</td>\n",
       "      <td>1666</td>\n",
       "      <td>daily_visits</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-07-05</td>\n",
       "      <td>1433</td>\n",
       "      <td>daily_visits</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-07-06</td>\n",
       "      <td>2195</td>\n",
       "      <td>daily_visits</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020-07-07</td>\n",
       "      <td>2240</td>\n",
       "      <td>daily_visits</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020-07-08</td>\n",
       "      <td>2295</td>\n",
       "      <td>daily_visits</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020-07-09</td>\n",
       "      <td>2279</td>\n",
       "      <td>daily_visits</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020-07-10</td>\n",
       "      <td>2155</td>\n",
       "      <td>daily_visits</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  users     unique_id\n",
       "0 2020-07-01   2324  daily_visits\n",
       "1 2020-07-02   2201  daily_visits\n",
       "2 2020-07-03   2146  daily_visits\n",
       "3 2020-07-04   1666  daily_visits\n",
       "4 2020-07-05   1433  daily_visits\n",
       "5 2020-07-06   2195  daily_visits\n",
       "6 2020-07-07   2240  daily_visits\n",
       "7 2020-07-08   2295  daily_visits\n",
       "8 2020-07-09   2279  daily_visits\n",
       "9 2020-07-10   2155  daily_visits"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = ('https://raw.githubusercontent.com/JoaquinAmatRodrigo/Estadistica-machine-learning-python/' +\n",
    "       'master/data/visitas_por_dia_web_cienciadedatos.csv')\n",
    "df = pd.read_csv(url, sep=',', parse_dates=[0], date_format='%d/%m/%y')\n",
    "df['unique_id'] = 'daily_visits'\n",
    "\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's it! No more preprocessing is necessary."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Cross-validation with TimeGPT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can perform cross-validation on our data as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:nixtla.nixtla_client:Validating inputs...\n",
      "INFO:nixtla.nixtla_client:Validating inputs...\n",
      "INFO:nixtla.nixtla_client:Preprocessing dataframes...\n",
      "INFO:nixtla.nixtla_client:Restricting input...\n",
      "INFO:nixtla.nixtla_client:Calling Forecast Endpoint...\n",
      "INFO:nixtla.nixtla_client:Validating inputs...\n",
      "INFO:nixtla.nixtla_client:Validating inputs...\n",
      "INFO:nixtla.nixtla_client:Preprocessing dataframes...\n",
      "INFO:nixtla.nixtla_client:Restricting input...\n",
      "INFO:nixtla.nixtla_client:Calling Forecast Endpoint...\n",
      "INFO:nixtla.nixtla_client:Validating inputs...\n",
      "INFO:nixtla.nixtla_client:Validating inputs...\n",
      "INFO:nixtla.nixtla_client:Preprocessing dataframes...\n",
      "INFO:nixtla.nixtla_client:Restricting input...\n",
      "INFO:nixtla.nixtla_client:Calling Forecast Endpoint...\n",
      "INFO:nixtla.nixtla_client:Validating inputs...\n",
      "INFO:nixtla.nixtla_client:Validating inputs...\n",
      "INFO:nixtla.nixtla_client:Preprocessing dataframes...\n",
      "INFO:nixtla.nixtla_client:Restricting input...\n",
      "INFO:nixtla.nixtla_client:Calling Forecast Endpoint...\n",
      "INFO:nixtla.nixtla_client:Validating inputs...\n",
      "INFO:nixtla.nixtla_client:Validating inputs...\n",
      "INFO:nixtla.nixtla_client:Preprocessing dataframes...\n",
      "INFO:nixtla.nixtla_client:Restricting input...\n",
      "INFO:nixtla.nixtla_client:Calling Forecast Endpoint...\n",
      "INFO:nixtla.nixtla_client:Validating inputs...\n",
      "INFO:nixtla.nixtla_client:Validating inputs...\n",
      "INFO:nixtla.nixtla_client:Preprocessing dataframes...\n",
      "INFO:nixtla.nixtla_client:Restricting input...\n",
      "INFO:nixtla.nixtla_client:Calling Forecast Endpoint...\n",
      "INFO:nixtla.nixtla_client:Validating inputs...\n",
      "INFO:nixtla.nixtla_client:Validating inputs...\n",
      "INFO:nixtla.nixtla_client:Preprocessing dataframes...\n",
      "INFO:nixtla.nixtla_client:Restricting input...\n",
      "INFO:nixtla.nixtla_client:Calling Forecast Endpoint...\n",
      "INFO:nixtla.nixtla_client:Validating inputs...\n",
      "INFO:nixtla.nixtla_client:Validating inputs...\n",
      "INFO:nixtla.nixtla_client:Preprocessing dataframes...\n",
      "INFO:nixtla.nixtla_client:Restricting input...\n",
      "INFO:nixtla.nixtla_client:Calling Forecast Endpoint...\n",
      "INFO:nixtla.nixtla_client:Validating inputs...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>date</th>\n",
       "      <th>cutoff</th>\n",
       "      <th>users</th>\n",
       "      <th>TimeGPT</th>\n",
       "      <th>TimeGPT-lo-99.5</th>\n",
       "      <th>TimeGPT-lo-90</th>\n",
       "      <th>TimeGPT-lo-80</th>\n",
       "      <th>TimeGPT-hi-80</th>\n",
       "      <th>TimeGPT-hi-90</th>\n",
       "      <th>TimeGPT-hi-99.5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>daily_visits</td>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>2021-06-30</td>\n",
       "      <td>3123</td>\n",
       "      <td>3310.908447</td>\n",
       "      <td>3041.925497</td>\n",
       "      <td>3048.363220</td>\n",
       "      <td>3082.721924</td>\n",
       "      <td>3539.094971</td>\n",
       "      <td>3573.453674</td>\n",
       "      <td>3579.891397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>daily_visits</td>\n",
       "      <td>2021-07-02</td>\n",
       "      <td>2021-06-30</td>\n",
       "      <td>2870</td>\n",
       "      <td>3090.971680</td>\n",
       "      <td>2793.535905</td>\n",
       "      <td>2838.480298</td>\n",
       "      <td>2853.750488</td>\n",
       "      <td>3328.192871</td>\n",
       "      <td>3343.463062</td>\n",
       "      <td>3388.407455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>daily_visits</td>\n",
       "      <td>2021-07-03</td>\n",
       "      <td>2021-06-30</td>\n",
       "      <td>2020</td>\n",
       "      <td>2346.991455</td>\n",
       "      <td>2043.731296</td>\n",
       "      <td>2150.005078</td>\n",
       "      <td>2171.187012</td>\n",
       "      <td>2522.795898</td>\n",
       "      <td>2543.977832</td>\n",
       "      <td>2650.251614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>daily_visits</td>\n",
       "      <td>2021-07-04</td>\n",
       "      <td>2021-06-30</td>\n",
       "      <td>1828</td>\n",
       "      <td>2182.191895</td>\n",
       "      <td>1836.848173</td>\n",
       "      <td>1897.684900</td>\n",
       "      <td>1929.914575</td>\n",
       "      <td>2434.469214</td>\n",
       "      <td>2466.698889</td>\n",
       "      <td>2527.535616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>daily_visits</td>\n",
       "      <td>2021-07-05</td>\n",
       "      <td>2021-06-30</td>\n",
       "      <td>2722</td>\n",
       "      <td>3082.715088</td>\n",
       "      <td>2736.008055</td>\n",
       "      <td>2746.997034</td>\n",
       "      <td>2791.375342</td>\n",
       "      <td>3374.054834</td>\n",
       "      <td>3418.433142</td>\n",
       "      <td>3429.422121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      unique_id        date     cutoff  users      TimeGPT  TimeGPT-lo-99.5  \\\n",
       "0  daily_visits  2021-07-01 2021-06-30   3123  3310.908447      3041.925497   \n",
       "1  daily_visits  2021-07-02 2021-06-30   2870  3090.971680      2793.535905   \n",
       "2  daily_visits  2021-07-03 2021-06-30   2020  2346.991455      2043.731296   \n",
       "3  daily_visits  2021-07-04 2021-06-30   1828  2182.191895      1836.848173   \n",
       "4  daily_visits  2021-07-05 2021-06-30   2722  3082.715088      2736.008055   \n",
       "\n",
       "   TimeGPT-lo-90  TimeGPT-lo-80  TimeGPT-hi-80  TimeGPT-hi-90  TimeGPT-hi-99.5  \n",
       "0    3048.363220    3082.721924    3539.094971    3573.453674      3579.891397  \n",
       "1    2838.480298    2853.750488    3328.192871    3343.463062      3388.407455  \n",
       "2    2150.005078    2171.187012    2522.795898    2543.977832      2650.251614  \n",
       "3    1897.684900    1929.914575    2434.469214    2466.698889      2527.535616  \n",
       "4    2746.997034    2791.375342    3374.054834    3418.433142      3429.422121  "
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timegpt_cv_df = nixtla_client.cross_validation(\n",
    "    df, \n",
    "    h=7, \n",
    "    n_windows=8, \n",
    "    time_col='date', \n",
    "    target_col='users', \n",
    "    freq='D',\n",
    "    level=[80, 90, 99.5]\n",
    ")\n",
    "timegpt_cv_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 📘 Available models in Azure AI\n",
    ">\n",
    "> If you are using an Azure AI endpoint, please be sure to set `model=\"azureai\"`:\n",
    ">\n",
    "> `nixtla_client.cross_validation(..., model=\"azureai\")`\n",
    "> \n",
    "> For the public API, we support two models: `timegpt-1` and `timegpt-1-long-horizon`. \n",
    "> \n",
    "> By default, `timegpt-1` is used. Please see [this tutorial](https://docs.nixtla.io/docs/tutorials-long_horizon_forecasting) on how and when to use `timegpt-1-long-horizon`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we have performed a rolling cross-validation of 8 folds. Let's plot the cross-validated forecasts including the prediction intervals:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x350 with 1 Axes>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nixtla_client.plot(\n",
    "    df, \n",
    "    timegpt_cv_df.drop(columns=['cutoff', 'users']), \n",
    "    time_col='date',\n",
    "    target_col='users',\n",
    "    max_insample_length=90, \n",
    "    level=[80, 90, 99.5]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This looks reasonable, and very comparable to the results obtained [here](https://cienciadedatos.net/documentos/py37-forecasting-web-traffic-machine-learning.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check the Mean Absolute Error of our cross-validation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utilsforecast.losses import mae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>TimeGPT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>daily_visits</td>\n",
       "      <td>167.691711</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      unique_id     TimeGPT\n",
       "0  daily_visits  167.691711"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mae_timegpt = mae(df = timegpt_cv_df.drop(columns=['cutoff']),\n",
    "    models=['TimeGPT'],\n",
    "    target_col='users')\n",
    "\n",
    "mae_timegpt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The MAE of our backtest is `167.69`. Hence, not only did TimeGPT achieve a lower MAE compared to the fully customized pipeline [here](https://cienciadedatos.net/documentos/py37-forecasting-web-traffic-machine-learning.html), the error of the forecast is also lower."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exogenous variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's add some exogenous variables to see if we can improve the forecasting performance further.\n",
    "\n",
    "We will add weekday indicators, which we will extract from the `date` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>users</th>\n",
       "      <th>unique_id</th>\n",
       "      <th>week_day_1</th>\n",
       "      <th>week_day_2</th>\n",
       "      <th>week_day_3</th>\n",
       "      <th>week_day_4</th>\n",
       "      <th>week_day_5</th>\n",
       "      <th>week_day_6</th>\n",
       "      <th>week_day_7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>2324</td>\n",
       "      <td>daily_visits</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2201</td>\n",
       "      <td>daily_visits</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-07-03</td>\n",
       "      <td>2146</td>\n",
       "      <td>daily_visits</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-07-04</td>\n",
       "      <td>1666</td>\n",
       "      <td>daily_visits</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-07-05</td>\n",
       "      <td>1433</td>\n",
       "      <td>daily_visits</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-07-06</td>\n",
       "      <td>2195</td>\n",
       "      <td>daily_visits</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020-07-07</td>\n",
       "      <td>2240</td>\n",
       "      <td>daily_visits</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020-07-08</td>\n",
       "      <td>2295</td>\n",
       "      <td>daily_visits</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020-07-09</td>\n",
       "      <td>2279</td>\n",
       "      <td>daily_visits</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020-07-10</td>\n",
       "      <td>2155</td>\n",
       "      <td>daily_visits</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  users     unique_id  week_day_1  week_day_2  week_day_3  \\\n",
       "0 2020-07-01   2324  daily_visits           0           0           1   \n",
       "1 2020-07-02   2201  daily_visits           0           0           0   \n",
       "2 2020-07-03   2146  daily_visits           0           0           0   \n",
       "3 2020-07-04   1666  daily_visits           0           0           0   \n",
       "4 2020-07-05   1433  daily_visits           0           0           0   \n",
       "5 2020-07-06   2195  daily_visits           1           0           0   \n",
       "6 2020-07-07   2240  daily_visits           0           1           0   \n",
       "7 2020-07-08   2295  daily_visits           0           0           1   \n",
       "8 2020-07-09   2279  daily_visits           0           0           0   \n",
       "9 2020-07-10   2155  daily_visits           0           0           0   \n",
       "\n",
       "   week_day_4  week_day_5  week_day_6  week_day_7  \n",
       "0           0           0           0           0  \n",
       "1           1           0           0           0  \n",
       "2           0           1           0           0  \n",
       "3           0           0           1           0  \n",
       "4           0           0           0           1  \n",
       "5           0           0           0           0  \n",
       "6           0           0           0           0  \n",
       "7           0           0           0           0  \n",
       "8           1           0           0           0  \n",
       "9           0           1           0           0  "
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We have 7 days, for each day a separate column denoting 1/0\n",
    "for i in range(7):\n",
    "    df[f'week_day_{i + 1}'] = 1 * (df['date'].dt.weekday == i)\n",
    "\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's rerun the cross-validation procedure with the added exogenous variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:nixtla.nixtla_client:Validating inputs...\n",
      "INFO:nixtla.nixtla_client:Validating inputs...\n",
      "INFO:nixtla.nixtla_client:Preprocessing dataframes...\n",
      "INFO:nixtla.nixtla_client:Using the following exogenous variables: week_day_1, week_day_2, week_day_3, week_day_4, week_day_5, week_day_6, week_day_7\n",
      "INFO:nixtla.nixtla_client:Calling Forecast Endpoint...\n",
      "INFO:nixtla.nixtla_client:Validating inputs...\n",
      "INFO:nixtla.nixtla_client:Validating inputs...\n",
      "INFO:nixtla.nixtla_client:Preprocessing dataframes...\n",
      "INFO:nixtla.nixtla_client:Using the following exogenous variables: week_day_1, week_day_2, week_day_3, week_day_4, week_day_5, week_day_6, week_day_7\n",
      "INFO:nixtla.nixtla_client:Calling Forecast Endpoint...\n",
      "INFO:nixtla.nixtla_client:Validating inputs...\n",
      "INFO:nixtla.nixtla_client:Validating inputs...\n",
      "INFO:nixtla.nixtla_client:Preprocessing dataframes...\n",
      "INFO:nixtla.nixtla_client:Using the following exogenous variables: week_day_1, week_day_2, week_day_3, week_day_4, week_day_5, week_day_6, week_day_7\n",
      "INFO:nixtla.nixtla_client:Calling Forecast Endpoint...\n",
      "INFO:nixtla.nixtla_client:Validating inputs...\n",
      "INFO:nixtla.nixtla_client:Validating inputs...\n",
      "INFO:nixtla.nixtla_client:Preprocessing dataframes...\n",
      "INFO:nixtla.nixtla_client:Using the following exogenous variables: week_day_1, week_day_2, week_day_3, week_day_4, week_day_5, week_day_6, week_day_7\n",
      "INFO:nixtla.nixtla_client:Calling Forecast Endpoint...\n",
      "INFO:nixtla.nixtla_client:Validating inputs...\n",
      "INFO:nixtla.nixtla_client:Validating inputs...\n",
      "INFO:nixtla.nixtla_client:Preprocessing dataframes...\n",
      "INFO:nixtla.nixtla_client:Using the following exogenous variables: week_day_1, week_day_2, week_day_3, week_day_4, week_day_5, week_day_6, week_day_7\n",
      "INFO:nixtla.nixtla_client:Calling Forecast Endpoint...\n",
      "INFO:nixtla.nixtla_client:Validating inputs...\n",
      "INFO:nixtla.nixtla_client:Validating inputs...\n",
      "INFO:nixtla.nixtla_client:Preprocessing dataframes...\n",
      "INFO:nixtla.nixtla_client:Using the following exogenous variables: week_day_1, week_day_2, week_day_3, week_day_4, week_day_5, week_day_6, week_day_7\n",
      "INFO:nixtla.nixtla_client:Calling Forecast Endpoint...\n",
      "INFO:nixtla.nixtla_client:Validating inputs...\n",
      "INFO:nixtla.nixtla_client:Validating inputs...\n",
      "INFO:nixtla.nixtla_client:Preprocessing dataframes...\n",
      "INFO:nixtla.nixtla_client:Using the following exogenous variables: week_day_1, week_day_2, week_day_3, week_day_4, week_day_5, week_day_6, week_day_7\n",
      "INFO:nixtla.nixtla_client:Calling Forecast Endpoint...\n",
      "INFO:nixtla.nixtla_client:Validating inputs...\n",
      "INFO:nixtla.nixtla_client:Validating inputs...\n",
      "INFO:nixtla.nixtla_client:Preprocessing dataframes...\n",
      "INFO:nixtla.nixtla_client:Using the following exogenous variables: week_day_1, week_day_2, week_day_3, week_day_4, week_day_5, week_day_6, week_day_7\n",
      "INFO:nixtla.nixtla_client:Calling Forecast Endpoint...\n",
      "INFO:nixtla.nixtla_client:Validating inputs...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>date</th>\n",
       "      <th>cutoff</th>\n",
       "      <th>users</th>\n",
       "      <th>TimeGPT</th>\n",
       "      <th>TimeGPT-lo-99.5</th>\n",
       "      <th>TimeGPT-lo-90</th>\n",
       "      <th>TimeGPT-lo-80</th>\n",
       "      <th>TimeGPT-hi-80</th>\n",
       "      <th>TimeGPT-hi-90</th>\n",
       "      <th>TimeGPT-hi-99.5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>daily_visits</td>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>2021-06-30</td>\n",
       "      <td>3123</td>\n",
       "      <td>3314.773743</td>\n",
       "      <td>2793.566942</td>\n",
       "      <td>3043.304261</td>\n",
       "      <td>3085.668122</td>\n",
       "      <td>3543.879364</td>\n",
       "      <td>3586.243226</td>\n",
       "      <td>3835.980544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>daily_visits</td>\n",
       "      <td>2021-07-02</td>\n",
       "      <td>2021-06-30</td>\n",
       "      <td>2870</td>\n",
       "      <td>3093.066529</td>\n",
       "      <td>2139.727892</td>\n",
       "      <td>2725.964112</td>\n",
       "      <td>2779.082154</td>\n",
       "      <td>3407.050904</td>\n",
       "      <td>3460.168946</td>\n",
       "      <td>4046.405166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>daily_visits</td>\n",
       "      <td>2021-07-03</td>\n",
       "      <td>2021-06-30</td>\n",
       "      <td>2020</td>\n",
       "      <td>2347.973573</td>\n",
       "      <td>1386.090529</td>\n",
       "      <td>1915.487550</td>\n",
       "      <td>1973.679628</td>\n",
       "      <td>2722.267519</td>\n",
       "      <td>2780.459596</td>\n",
       "      <td>3309.856618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>daily_visits</td>\n",
       "      <td>2021-07-04</td>\n",
       "      <td>2021-06-30</td>\n",
       "      <td>1828</td>\n",
       "      <td>2182.467408</td>\n",
       "      <td>1003.677454</td>\n",
       "      <td>1681.246491</td>\n",
       "      <td>1874.572327</td>\n",
       "      <td>2490.362488</td>\n",
       "      <td>2683.688324</td>\n",
       "      <td>3361.257361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>daily_visits</td>\n",
       "      <td>2021-07-05</td>\n",
       "      <td>2021-06-30</td>\n",
       "      <td>2722</td>\n",
       "      <td>3083.629453</td>\n",
       "      <td>1257.248435</td>\n",
       "      <td>2220.430357</td>\n",
       "      <td>2556.408628</td>\n",
       "      <td>3610.850279</td>\n",
       "      <td>3946.828550</td>\n",
       "      <td>4910.010472</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      unique_id        date     cutoff  users      TimeGPT  TimeGPT-lo-99.5  \\\n",
       "0  daily_visits  2021-07-01 2021-06-30   3123  3314.773743      2793.566942   \n",
       "1  daily_visits  2021-07-02 2021-06-30   2870  3093.066529      2139.727892   \n",
       "2  daily_visits  2021-07-03 2021-06-30   2020  2347.973573      1386.090529   \n",
       "3  daily_visits  2021-07-04 2021-06-30   1828  2182.467408      1003.677454   \n",
       "4  daily_visits  2021-07-05 2021-06-30   2722  3083.629453      1257.248435   \n",
       "\n",
       "   TimeGPT-lo-90  TimeGPT-lo-80  TimeGPT-hi-80  TimeGPT-hi-90  TimeGPT-hi-99.5  \n",
       "0    3043.304261    3085.668122    3543.879364    3586.243226      3835.980544  \n",
       "1    2725.964112    2779.082154    3407.050904    3460.168946      4046.405166  \n",
       "2    1915.487550    1973.679628    2722.267519    2780.459596      3309.856618  \n",
       "3    1681.246491    1874.572327    2490.362488    2683.688324      3361.257361  \n",
       "4    2220.430357    2556.408628    3610.850279    3946.828550      4910.010472  "
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timegpt_cv_df_with_ex = nixtla_client.cross_validation(\n",
    "    df, \n",
    "    h=7, \n",
    "    n_windows=8, \n",
    "    time_col='date', \n",
    "    target_col='users', \n",
    "    freq='D',\n",
    "    level=[80, 90, 99.5]\n",
    ")\n",
    "timegpt_cv_df_with_ex.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot our forecasts again and calculate our error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x350 with 1 Axes>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nixtla_client.plot(\n",
    "    df, \n",
    "    timegpt_cv_df_with_ex.drop(columns=['cutoff', 'users']), \n",
    "    time_col='date',\n",
    "    target_col='users',\n",
    "    max_insample_length=90, \n",
    "    level=[80, 90, 99.5]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>TimeGPT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>daily_visits</td>\n",
       "      <td>167.22857</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      unique_id    TimeGPT\n",
       "0  daily_visits  167.22857"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mae_timegpt_with_exogenous = mae(df = timegpt_cv_df_with_ex.drop(columns=['cutoff']),\n",
    "    models=['TimeGPT'],\n",
    "    target_col='users')\n",
    "\n",
    "mae_timegpt_with_exogenous"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To conclude, we obtain the following forecast results in this notebook:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>Exogenous features</th>\n",
       "      <th>MAE backtest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TimeGPT</td>\n",
       "      <td>False</td>\n",
       "      <td>167.691711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TimeGPT</td>\n",
       "      <td>True</td>\n",
       "      <td>167.228570</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     model  Exogenous features  MAE backtest\n",
       "0  TimeGPT               False    167.691711\n",
       "0  TimeGPT                True    167.228570"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mae_timegpt['Exogenous features'] = False\n",
    "mae_timegpt_with_exogenous['Exogenous features'] = True\n",
    "\n",
    "df_results = pd.concat([mae_timegpt, mae_timegpt_with_exogenous])\n",
    "df_results = df_results.rename(columns={'TimeGPT':'MAE backtest'})\n",
    "df_results = df_results.drop(columns={'unique_id'})\n",
    "df_results['model'] = 'TimeGPT'\n",
    "\n",
    "df_results[['model', 'Exogenous features', 'MAE backtest']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We've shown how to forecast daily visits of a website. We achieved almost 10% better forecasting results as compared to the [original tutorial](https://cienciadedatos.net/documentos/py37-forecasting-web-traffic-machine-learning.html), using significantly less lines of code, in a fraction of the time required to run everything.\n",
    "\n",
    "Did you notice how little effort that took? What you did not have to do, is:\n",
    "\n",
    "- Elaborate data preprocessing - just a table with timeseries is sufficient\n",
    "- Creating a validation- and test set - TimeGPT handles the cross-validation in a single function\n",
    "- Choosing and testing different models - It's just a single call to TimeGPT\n",
    "- Hyperparameter tuning - Not necessary.\n",
    "\n",
    "Happy forecasting!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "python3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
